Assessing the efficacy of interventions to control indoor SARS-Cov-2 transmission: An agent-based modeling approach

Agent-based modeling to estimate the impact of lockdown scenarios and events on a pandemic exemplified on SARS-CoV-2

In actual pandemic situations like COVID-19, it is important to understand the influence of single mitigation measures as well as combinations to create most dynamic impact for lockdown scenarios. Therefore we created an agent-based model (ABM) to simulate the spread of SARS-CoV-2 in an abstract city model with several types of places and agents. In comparison to infection numbers in Germany our ABM could be shown to behave similarly during the first wave. In our model, we implemented the possibility to test the effectiveness of mitigation measures and lockdown scenarios on the course of the pandemic. In this context, we focused on parameters of local events as possible mitigation measures and ran simulations, including varying size, duration, frequency and the proportion of events. The majority of changes to single event parameters, with the exception of frequency, showed only a small influence on the overall course of the pandemic. By applying different lockdown scenarios in our simulations, we could observe drastic changes in the number of infections per day. Depending on the lockdown strategy, we even observed a delayed peak in infection numbers of the second wave. As an advantage of the developed ABM, it is possible to analyze the individual risk of single agents during the pandemic. In contrast to standard or adjusted ODEs, we observed a 21% (with masks) / 48% (without masks) increased risk for single reappearing participants on local events, with a linearly increasing risk based on the length of the events.

Efficacy of facemasks in preventing transmission of COVID-19 in non-healthcare settings: A scoping review

Background

During the COVID-19 pandemic, an abundance of literature relating to the efficacy of face masks on reducing transmission of COVID-19 in non-healthcare settings emerged.

Aim/objective

The aim of this scoping review was to allow the identification of: types of evidence conducted in this area; knowledge gaps and common concepts relating to mask efficacy in non-healthcare settings.

Methods

A comprehensive literature search was conducted in PubMed, CINAHL, MEDLINE, Embase and the Irish Management Institute bibliographic database on December 15th, 2021. All types of face masks were included. Of 722 records, 16 were included after full text screening.

Findings/results

Themes from an adapted model of Howard et al. framework were used to group results and identify common concepts. The grouped thematic results were then applied to the socio-ecological model. This illustrated the multifactorial elements determining the efficacy of masks themselves while also illustrating how other factors such as individual behaviours, social interactions, settings and national policy can influence the degree of the protective effect.

Discussion

The findings from this scoping review indicate that an abundance of experimental literature is available indicating that masks are effective at preventing COVID-19 transmission but their degree of efficacy is impacted by external factors. This review highlights that the quality of the evidence available is low.

Combined Infection Control Interventions Protect Essential Food Workers from Occupational Exposures to SARS-CoV-2 in the Agricultural Environment

Essential food workers experience elevated risks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection due to prolonged occupational exposures in food production and processing areas, shared transportation (car or bus), and employer-provided shared housing. Our goal was to quantify the daily cumulative risk of SARS-CoV-2 infection for healthy susceptible produce workers and to evaluate the relative reduction in risk attributable to food industry interventions and vaccination. We simulated daily SARS-CoV-2 exposures of indoor and outdoor produce workers through six linked quantitative microbial risk assessment (QMRA) model scenarios. For each scenario, the infectious viral dose emitted by a symptomatic worker was calculated across aerosol, droplet, and fomite-mediated transmission pathways. Standard industry interventions (2-m physical distancing, handwashing, surface disinfection, universal masking, ventilation) were simulated to assess relative risk reductions from baseline risk (no interventions, 1-m distance). Implementation of industry interventions reduced an indoor worker's relative infection risk by 98.0% (0.020; 95% uncertainty interval [UI], 0.005 to 0.104) from baseline risk (1.00; 95% UI, 0.995 to 1.00) and an outdoor worker's relative infection risk by 94.5% (0.027; 95% UI, 0.013 to 0.055) from baseline risk (0.487; 95% UI, 0.257 to 0.825). Integrating these interventions with two-dose mRNA vaccinations (86 to 99% efficacy), representing a worker's protective immunity to infection, reduced the relative infection risk from baseline for indoor workers by 99.9% (0.001; 95% UI, 0.0002 to 0.005) and outdoor workers by 99.6% (0.002; 95% UI, 0.0003 to 0.005). Consistent implementation of combined industry interventions, paired with vaccination, effectively mitigates the elevated risks from occupationally acquired SARS-CoV-2 infection faced by produce workers. IMPORTANCE This is the first study to estimate the daily risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection across a variety of indoor and outdoor environmental settings relevant to food workers (e.g., shared transportation [car or bus], enclosed produce processing facility and accompanying breakroom, outdoor produce harvesting field, shared housing facility) through a linked quantitative microbial risk assessment framework. Our model has demonstrated that the elevated daily SARS-CoV-2 infection risk experienced by indoor and outdoor produce workers can be reduced below 1% when vaccinations (optimal vaccine efficacy, 86 to 99%) are implemented with recommended infection control strategies (e.g., handwashing, surface disinfection, universal masking, physical distancing, and increased ventilation). Our novel findings provide scenario-specific infection risk estimates that can be utilized by food industry managers to target high-risk scenarios with effective infection mitigation strategies, which was informed through more realistic and context-driven modeling estimates of the infection risk faced by essential food workers daily. Bundled interventions, particularly if they include vaccination, yield significant reductions (>99%) in daily SARS-CoV-2 infection risk for essential food workers in enclosed and open-air environments.

Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review

The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.

The Skagit County choir COVID-19 outbreak - have we got it wrong?

OBJECTIVES

Over time, papers or reports may come to be taken for granted as evidence for some phenomenon. Researchers cite them without critically re-examining findings in the light of subsequent work. This can give rise to misleading or erroneous results and conclusions. We explore whether this has occurred in the widely reported outbreak of SARS-CoV-2 at a rehearsal of the Skagit Valley Chorale in March 2020, where it was assumed, and subsequently asserted uncritically, that the outbreak was due to a single infected person.

STUDY DESIGN

Review of original report and subsequent modelling and interpretations.

METHODS

We reviewed and analysed original outbreak data in relation to published data on incubation period, subsequent modelling drawing on the data, and interpretations of transmission characteristics of this incident.

RESULTS

We show it is vanishingly unlikely that this was a single point source outbreak as has been widely claimed and on which modelling has been based.

CONCLUSION

An unexamined assumption has led to erroneous policy conclusions about the risks of singing, and indoor spaces more generally, and the benefits of increased levels of ventilation. Although never publicly identified, one individual bears the moral burden of knowing what health outcomes have been attributed to their actions. We call for these claims to be re-examined and for greater ethical responsibility in the assumption of a point source in outbreak investigations.

Using a real-world network to model the trade-off between stay-at-home restriction, vaccination, social distancing and working hours on COVID-19 dynamics

Background

Human behaviour, economic activity, vaccination, and social distancing are inseparably entangled in epidemic management. This study aims to investigate the effects of various parameters such as stay-at-home restrictions, work hours, vaccination, and social distance on the containment of pandemics such as COVID-19.

Methods

To achieve this, we have developed an agent based model based on a time-dynamic graph with stochastic transmission events. The graph is constructed from a real-world social network. The edges of graph have been categorized into three categories: home, workplaces, and social environment. The conditions needed to mitigate the spread of wild-type COVID-19 and the delta variant have been analyzed. Our purposeful agent based model has carefully executed tens of thousands of individual-based simulations. We propose simple relationships for the trade-offs between effective reproduction number (R _e), transmission rate, working hours, vaccination, and stay-at-home restrictions.

Results

We have found that the effect of a 13.6% increase in vaccination for wild-type (WT) COVID-19 is equivalent to reducing four hours of work or a one-day stay-at-home restriction. For the delta, 20.2% vaccination has the same effect. Also, since we can keep track of household and non-household infections, we observed that the change in household transmission rate does not significantly alter the R _e. Household infections are not limited by transmission rate due to the high frequency of connections. For the specifications of COVID-19, the R _e depends on the non-household transmissions rate.

Conclusions

Our findings highlight that decreasing working hours is the least effective among the non-pharmaceutical interventions. Our results suggest that policymakers decrease work-related activities as a last resort and should probably not do so when the effects are minimal, as shown. Furthermore, the enforcement of stay-at-home restrictions is moderately effective and can be used in conjunction with other measures if absolutely necessary.

The impact of crowd gatherings on the spread of COVID-19

Crowd gatherings are an important cause of COVID-19 outbreaks. However, how the scale, scene and other factors of gatherings affect the spread of the epidemic remains unclear. A total of 184 gathering events worldwide were collected to construct a database, and 99 of them with a clear gathering scale were used for statistical analysis of the impact of these factors on the disease incidence among the crowd in the study. The results showed that the impact of small-scale (less than 100 people) gathering events on the spread of COVID-19 in the city is also not to be underestimated due to their characteristics of more frequent occurrence and less detection and control. In our dataset, 22.22% of small-scale events have an incidence of more than 0.8. In contrast, the incidence of most large-scale events is less than 0.4. Gathering scenes such as "Meal" and "Family" occur in densely populated private or small public places have the highest incidence. We further designed a model of epidemic transmission triggered by crowd gathering events and simulated the impact of crowd gathering events on the overall epidemic situation in the city. The simulation results showed that the number of patients will be drastically reduced if the scale and the density of crowds gathering are halved. It indicated that crowd gatherings should be strictly controlled on a small scale. In addition, it showed that the model well reproduce the epidemic spread after crowd gathering events better than does the original SIER model and could be applied to epidemic prediction after sudden gathering events.

Using simulation modelling and systems science to help contain COVID-19: A systematic review

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.

COVID-19 aerosol transmission simulation-based risk analysis for in-person learning

As educational institutions begin a school year following a year and a half of disruption from the COVID-19 pandemic, risk analysis can help to support decision-making for resuming in-person instructional operation by providing estimates of the relative risk reduction due to different interventions. In particular, a simulation-based risk analysis approach enables scenario evaluation and comparison to guide decision making and action prioritization under uncertainty. We develop a simulation model to characterize the risks and uncertainties associated with infections resulting from aerosol exposure in in-person classes. We demonstrate this approach by applying it to model a semester of courses in a real college with approximately 11,000 students embedded within a larger university. To have practical impact, risk cannot focus on only infections as the end point of interest, we estimate the risks of infection, hospitalizations, and deaths of students and faculty in the college. We incorporate uncertainties in disease transmission, the impact of policies such as masking and facility interventions, and variables outside of the college’s control such as population-level disease and immunity prevalence. We show in our example application that universal use of masks that block 40% of aerosols and the installation of near-ceiling, fan-mounted UVC systems both have the potential to lead to substantial risk reductions and that these effects can be modeled at the individual room level. These results exemplify how such simulation-based risk analysis can inform decision making and prioritization under great uncertainty.

Impact of improved indoor environment on recovery from COVID-19 infections: a review of literature

Purpose

This study aims to explore the impact of the indoor environment on recovery from COVID-19 infections. Extant literature on the impact of the four key themes of the indoor environment (indoor air quality, indoor thermal quality, daylighting and visual comfort, and acoustic comfort) on COVID-19 infection and recovery rates were reviewed.

Design/methodology/approach

Data collection for this study was based on extant literature within the Scopus database and scoped to a time frame of 2020–2021 because the topical issue of indoor environmental quality (IEQ) and its impact on COVID-19 arose in the wake of the pandemic. In total, 224 documents were systematically desk reviewed from various journals.

Findings

The study identified that air pollutants such as PM2.5 and PM10 as well as air-conditioned places, low ambient temperatures, poor ventilation and no views of the outdoor environment were deteriorating factors for COVID-19 patients. On the other hand, proper ventilation, the use of air cleaners, views of the outdoor environment and allowance for ample daylighting were improvement factors for COVID-19 patients. The inter-relationship of the various concepts was presented in an ontology chart.

Practical implications

As COVID-19 still exists and keeps evolving, this study provides suggestions to industry professionals, especially health-care Facility Managers, to create a post-pandemic environment focusing on the IEQ and finding long-term and reliable solutions for the well-being of occupants. Adaptability is crucial. New, creative technology solutions are being introduced daily, but it is up to the facility managers and health-care professionals to analyse and specify the most cost- and outcome-effective technologies for their facility.

Originality/value

The study brought to light the pivotal role of the indoor environment on the health and well-being of occupants, particularly in the contraction, spread, prevention and control of infectious diseases such as COVID-19.

Risk Stratification of SARS-CoV-2 Breakthrough Infections Based on an Outbreak at a Student Festive Event

In early 2022, the Coronavirus disease 2019 (COVID-19) remains a global challenge. COVID-19 is caused by an increasing number of variants of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Here, we report an outbreak of SARS-CoV-2 breakthrough infections related to a student festive event with 100 mostly vaccinated guests, which took place in Northern Bavaria, Germany, in October 2021. The data were obtained by retrospective guest interviews. In total, 95 students participated in the study, with 94 being fully vaccinated and 24 reporting infection by the delta variant. Correlation analyses among 15 examined variables revealed that time spent at the event, conversation with the supposed index person, and a homologous viral vector vaccination regime were significant risk factors for infection. Non-significant observations related to higher rates of infection included time since last vaccination, shared use of drinking vessels, and number of individual person-to-person contacts at the event. Our data suggest that a high rate of breakthrough infections with the delta variant occurs if no preventive measures are practiced. To limit infection risk, high-quality testing of participants should be considered a mandatory measure at gatherings, irrespective of the participants’ vaccination status.

A Vaccination Simulator for COVID-19: Effective and Sterilizing Immunization Cases

In this work, we present a particle-based SEIR epidemic simulator as a tool to assess the impact of different vaccination strategies on viral propagation and to model sterilizing and effective immunization outcomes. The simulator includes modules to support contact tracing of the interactions amongst individuals and epidemiological testing of the general population. The particles are distinguished by age to represent more accurately the infection and mortality rates. The tool can be calibrated by region of interest and for different vaccination strategies to enable locality-sensitive virus mitigation policy measures and resource allocation. Moreover, the vaccination policy can be simulated based on the prioritization of certain age groups or randomly vaccinating individuals across all age groups. The results based on the experience of the province of Lecco, Italy, indicate that the simulator can evaluate vaccination strategies in a way that incorporates local circumstances of viral propagation and demographic susceptibilities. Further, the simulator accounts for modeling the distinction between sterilizing immunization, where immunized people are no longer contagious, and effective immunization, where the individuals can transmit the virus even after getting immunized. The parametric simulation results showed that the sterilizing-age-based vaccination scenario results in the least number of deaths. Furthermore, it revealed that older people should be vaccinated first to decrease the overall mortality rate. Also, the results showed that as the vaccination rate increases, the mortality rate between the scenarios shrinks.